http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout – Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host – Host name of Qdrant service. If url and host are None, set to
‘localhost’. Default: None
collection_name – Name of the Qdrant collection to be used. If not provided,
will be created randomly.
distance_func – Distance function. One of the: “Cosine” / “Euclid” / “Dot”.
content_payload_key – A payload key used to store the content of the document.
metadata_payload_key – A payload key used to store the metadata of the document.
**kwargs – Additional arguments passed directly into REST client initialization
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the Qdrant database
This is intended to be a quick way to get started.
Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20) → List[langchain.docstore.document.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
Returns
List of Documents selected by maximal marginal relevance.